Traditional, empirical ground‐motion models (GMMs) are developed by prescribing a functional form between predictive parameters and ground‐motion intensity measures. Machine‐learning techniques may serve as a fully data‐driven alternative to widely used regression techniques, as they do not require explicitly defining these relationships. Although, machine‐learning methods offer a nonparametric alternative to regression methods, there are few studies that develop and assess performance of traditional versus machine‐learning GMMs side by side. We compare the performance and behavior of these two approaches: a mixed‐effects maximum‐likelihood (MEML) model and a feed‐forward artificial neural network (ANN). We develop and train both models on the same dataset from southern California. We subsequently test both models on a dataset from the 2019 Ridgecrest sequence, in a new region and on magnitudes outside the range of the training dataset, to examine model portability. Our models estimate horizontal peak ground acceleration, and the input parameters include moment magnitude (M) and hypocentral distance (Rhyp), and some include a site parameter, either VS30 or κ0.

We find that, with our small set of input parameters, the ANN generally shows more site‐specific predictions than the MEML model with more variation between sites, and, performs better than their corresponding MEML model, when applied “blind” to our testing dataset (in which the MEML random effects cannot be considered). Although, previous studies have found that κ0 may be a better predictor of site effects than VS30, we found similar performance, suggesting that including a site parameter may be more important than the physical meaning of the parameter. Finally, when applying our models to our Ridgecrest dataset, we find that both methods perform well; however, the MEML models perform better with the new dataset than the ANN models, suggesting that future applications of ANN models may need to consider how to accommodate model portability.

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